Forced-dissipative barotropic QG beta-plane turbulence

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A simulation of forced-dissipative barotropic quasi-geostrophic turbulence on a beta plane. The dynamics include linear drag and stochastic excitation.

Install dependencies

First let's make sure we have all required packages installed.

using Pkg
pkg"add GeophysicalFlows, Random, Printf, Plots, Statistics"

Let's begin

Let's load GeophysicalFlows.jl and some other needed packages.

using GeophysicalFlows, Random, Printf, Plots

using FourierFlows: parsevalsum
using Statistics: mean

Choosing a device: CPU or GPU

dev = CPU()     # Device (CPU/GPU)

Numerical parameters and time-stepping parameters

      n = 128            # 2D resolution: n² grid points
stepper = "FilteredRK4"  # timestepper
     dt = 0.05           # timestep
 nsteps = 8000           # total number of time-steps
 nsubs  = 10             # number of time-steps for intermediate logging/plotting (nsteps must be multiple of nsubs)

Physical parameters

L = 2π        # domain size
β = 10.0      # planetary PV gradient
μ = 0.01      # bottom drag

Forcing

We force the vorticity equation with stochastic excitation that is delta-correlated in time and while spatially homogeneously and isotropically correlated. The forcing has a spectrum with power in a ring in wavenumber space of radius $k_f$ (forcing_wavenumber) and width $δ_f$ (forcing_bandwidth), and it injects energy per unit area and per unit time equal to $\varepsilon$. That is, the forcing covariance spectrum is proportional to $\exp{[-(|\bm{k}| - k_f)^2 / (2 δ_f^2)]}$.

forcing_wavenumber = 14.0 * 2π/L  # the forcing wavenumber, `k_f`, for a spectrum that is a ring in wavenumber space
forcing_bandwidth  = 1.5  * 2π/L  # the width of the forcing spectrum, `δ_f`
ε = 0.001                         # energy input rate by the forcing

grid = TwoDGrid(dev, n, L)

K = @. sqrt(grid.Krsq)            # a 2D array with the total wavenumber

forcing_spectrum = @. exp(-(K - forcing_wavenumber)^2 / (2 * forcing_bandwidth^2))
ε0 = parsevalsum(forcing_spectrum .* grid.invKrsq / 2, grid) / (grid.Lx * grid.Ly)
@. forcing_spectrum *= ε/ε0       # normalize forcing to inject energy at rate ε

We reset of the random number generator for reproducibility

if dev==CPU(); Random.seed!(1234); else; CUDA.seed!(1234); end

Next we construct function calcF! that computes a forcing realization every timestep. First we make sure that if dev=GPU(), then CUDA.rand() function is called for random numbers uniformly distributed between 0 and 1.

random_uniform = dev==CPU() ? rand : CUDA.rand

function calcF!(Fh, sol, t, clock, vars, params, grid)
  Fh .= sqrt.(forcing_spectrum) .* exp.(2π * im * random_uniform(eltype(grid), size(sol))) ./ sqrt(clock.dt)

  @CUDA.allowscalar Fh[1, 1] = 0 # make sure forcing has zero domain-average

  return nothing
end

Problem setup

We initialize a Problem by providing a set of keyword arguments. Not providing a viscosity coefficient ν leads to the module's default value: ν=0. In this example numerical instability due to accumulation of enstrophy in high wavenumbers is taken care with the FilteredTimestepper we picked.

prob = SingleLayerQG.Problem(dev; nx=n, Lx=L, β=β, μ=μ, dt=dt, stepper=stepper,
                             calcF=calcF!, stochastic=true)

Let's define some shortcuts.

sol, clock, vars, params, grid = prob.sol, prob.clock, prob.vars, prob.params, prob.grid
x, y = grid.x, grid.y

First let's see how a forcing realization looks like. Note that when plotting, we decorate the variable to be plotted with Array() to make sure it is brought back on the CPU when vars live on the GPU.

calcF!(vars.Fh, sol, 0.0, clock, vars, params, grid)

heatmap(x, y, Array(irfft(vars.Fh, grid.nx)'),
     aspectratio = 1,
               c = :balance,
            clim = (-8, 8),
           xlims = (-grid.Lx/2, grid.Lx/2),
           ylims = (-grid.Ly/2, grid.Ly/2),
          xticks = -3:3,
          yticks = -3:3,
          xlabel = "x",
          ylabel = "y",
           title = "a forcing realization",
      framestyle = :box)

Setting initial conditions

Our initial condition is simply fluid at rest.

SingleLayerQG.set_q!(prob, ArrayType(dev)(zeros(grid.nx, grid.ny)))

Diagnostics

Create Diagnostic – energy and enstrophy are functions imported at the top.

E = Diagnostic(SingleLayerQG.energy, prob; nsteps=nsteps)
Z = Diagnostic(SingleLayerQG.enstrophy, prob; nsteps=nsteps)
diags = [E, Z] # A list of Diagnostics types passed to "stepforward!" will  be updated every timestep.

Output

We choose folder for outputing .jld2 files and snapshots (.png files).

filepath = "."
plotpath = "./plots_forcedbetaturb"
plotname = "snapshots"
filename = joinpath(filepath, "forcedbetaturb.jld2")

Do some basic file management,

if isfile(filename); rm(filename); end
if !isdir(plotpath); mkdir(plotpath); end

and then create Output.

get_sol(prob) = sol # extracts the Fourier-transformed solution
get_u(prob) = irfft(im * grid.l .* grid.invKrsq .* sol, grid.nx)
out = Output(prob, filename, (:sol, get_sol), (:u, get_u))

Visualizing the simulation

We define a function that plots the vorticity and streamfunction fields, their corresponding zonal mean structure and timeseries of energy and enstrophy.

function plot_output(prob)
  q = prob.vars.q
  ψ = prob.vars.ψ
  q̄ = mean(q, dims=1)'
  ū = mean(prob.vars.u, dims=1)'

  pq = heatmap(x, y, Array(q'),
       aspectratio = 1,
            legend = false,
                 c = :balance,
              clim = (-8, 8),
             xlims = (-grid.Lx/2, grid.Lx/2),
             ylims = (-grid.Ly/2, grid.Ly/2),
            xticks = -3:3,
            yticks = -3:3,
            xlabel = "x",
            ylabel = "y",
             title = "vorticity ∂v/∂x-∂u/∂y",
        framestyle = :box)

  pψ = contourf(x, y, Array(ψ'),
            levels = -0.32:0.04:0.32,
       aspectratio = 1,
         linewidth = 1,
            legend = false,
              clim = (-0.22, 0.22),
                 c = :viridis,
             xlims = (-grid.Lx/2, grid.Lx/2),
             ylims = (-grid.Ly/2, grid.Ly/2),
            xticks = -3:3,
            yticks = -3:3,
            xlabel = "x",
            ylabel = "y",
             title = "streamfunction ψ",
        framestyle = :box)

  pqm = plot(Array(q̄), y,
            legend = false,
         linewidth = 2,
             alpha = 0.7,
            yticks = -3:3,
             xlims = (-3, 3),
            xlabel = "zonal mean q",
            ylabel = "y")
  plot!(pqm, 0*y, y, linestyle=:dash, linecolor=:black)

  pum = plot(Array(ū), y,
            legend = false,
         linewidth = 2,
             alpha = 0.7,
            yticks = -3:3,
             xlims = (-0.5, 0.5),
            xlabel = "zonal mean u",
            ylabel = "y")
  plot!(pum, 0*y, y, linestyle=:dash, linecolor=:black)

  pE = plot(1,
             label = "energy",
         linewidth = 2,
             alpha = 0.7,
             xlims = (-0.1, 4.1),
             ylims = (0, 0.05),
            xlabel = "μt")

  pZ = plot(1,
             label = "enstrophy",
         linecolor = :red,
            legend = :bottomright,
         linewidth = 2,
             alpha = 0.7,
             xlims = (-0.1, 4.1),
             ylims = (0, 2.5),
            xlabel = "μt")

  l = @layout Plots.grid(2, 3)
  p = plot(pq, pqm, pE, pψ, pum, pZ, layout=l, size = (1000, 600))

  return p
end

Time-stepping the Problem forward

We time-step the Problem forward in time.

startwalltime = time()

p = plot_output(prob)

anim = @animate for j = 0:Int(nsteps / nsubs)

  if j % (1000 / nsubs) == 0
    cfl = clock.dt * maximum([maximum(vars.u) / grid.dx, maximum(vars.v) / grid.dy])

    log = @sprintf("step: %04d, t: %d, cfl: %.2f, E: %.4f, Q: %.4f, walltime: %.2f min",
    clock.step, clock.t, cfl, E.data[E.i], Z.data[Z.i], (time()-startwalltime)/60)

    println(log)
  end

  p[1][1][:z] = Array(vars.q)
  p[1][:title] = "vorticity, μt="*@sprintf("%.2f", μ * clock.t)
  p[4][1][:z] = Array(vars.ψ)
  p[2][1][:x] = Array(mean(vars.q, dims=1)')
  p[5][1][:x] = Array(mean(vars.u, dims=1)')
  push!(p[3][1], μ * E.t[E.i], E.data[E.i])
  push!(p[6][1], μ * Z.t[Z.i], Z.data[Z.i])

  stepforward!(prob, diags, nsubs)
  SingleLayerQG.updatevars!(prob)
end

mp4(anim, "singlelayerqg_betaforced.mp4", fps=18)

Save

Finally, we can save, e.g., the last snapshot via

savename = @sprintf("%s_%09d.png", joinpath(plotpath, plotname), clock.step)
savefig(savename)

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